Machine-learning crop-type mapping sensitivity to feature selection and hyperparameter tuning Perez-Flores, M. /Satgé, Frédéric Molina-Carpio, J. /Hostache, Renaud Pillco-Zolá, R. Tola, D. Uscamayta-Ferrano, E. Bustillos, L. /Bonnet, Marie-Paule /Duwig, Céline crop-type mapping sentinel machine learning Bolivia Altiplano Highlights What are the main findings? Crop-type mapping reliability is highly sensitive to features selection and hyperparameter tuning. The model and target independent VIF feature selection is not recommended for crop-type mapping What are the implications of the main findings? Most reliable crop-type mapping is obtained through a proposed three-step process combining wrapped features selection with hyperparameter tuning. Based on open-access data and software, the proposed method can be used to support agriculture monitoring in a complex socio-economic context. Highlights What are the main findings? Crop-type mapping reliability is highly sensitive to features selection and hyperparameter tuning. The model and target independent VIF feature selection is not recommended for crop-type mapping What are the implications of the main findings? Most reliable crop-type mapping is obtained through a proposed three-step process combining wrapped features selection with hyperparameter tuning. Based on open-access data and software, the proposed method can be used to support agriculture monitoring in a complex socio-economic context.Abstract To improve crop yields and incomes, farmers consistently adapt their practices to climate and market fluctuations, resulting in highly variable crop field distribution and coverage in space and time. As these dynamics illustrate farmers' challenges, up-to-date crop-type mapping is essential for understanding farmers' needs and supporting their adoption of sustainable practices. With global coverage and frequent temporal observations, remote sensing data are generally integrated into machine learning models to monitor crop dynamics. Unlike physical-based models that rely on straightforward use, implementing machine learning models requires extensive user interaction. In this context, this study assesses how sensitive the models' outputs are to feature selection and hyperparameter tuning, as both processes rely on user judgment. To achieve this, Sentinel-1 (S1) and Sentinel-2 (S2) features are integrated into five distinct models (Random Forest (RF), Support Vector Machine (SVM), Light Gradient Boosting (LGB), Histogram-based Gradient Boosting (HGB), and Extreme Gradient Boosting (XGB)), considering several features selection (Variance Inflation Factor (VIF) and Sequential Feature Selector (SFS)) and hyperparameter tuning (Grid-Search) setup. Results show that the preprocess modeling feature selection (VIF) discards the features that the wrapped method (SFS) keeps, resulting in less reliable crop-type mapping. Additionally, hyperparameter tuning appears to be sensitive to the input features, and considering it after any feature selection improved the crop-type mapping. In this context a three-step nested modeling setup, including first hyperparameter tuning, followed by a wrapped feature selection (SFS) and additional hyperparameter tuning, leads to the most reliable model outputs. For the study region, LGB and XGB (SVM) are the most (least) suitable models for crop-type mapping, and model reliability improves when integrating S1 and S2 features rather than considering S1 or S2 alone. Finally, crop-type maps are derived across different regions and time periods to highlight the benefits of the proposed method for monitoring crop dynamics in space and time. 2026 text https://www.documentation.ird.fr/hor/fdi:010096465 fdi:010096465 Perez-Flores M., Satgé Frédéric, Molina-Carpio J., Hostache Renaud, Pillco-Zolá R., Tola D., Uscamayta-Ferrano E., Bustillos L., Bonnet Marie-Paule, Duwig Céline. Machine-learning crop-type mapping sensitivity to feature selection and hyperparameter tuning. 2026, 18 (4), 563 [23 p.] EN BOLIVIE